I Need Data - For Research

Overview

Join us as we delve into the complexities of securing high-quality data for artificial intelligence applications, particularly within the realm of security and other notable sectors. This event will shed light on various data acquisition methodologies and emphasize the importance of thorough quality assessment to foster effective research and development.

Hosted on YouTube, this enlightening presentation is a must-attend for anyone engaged in artificial intelligence research or looking to enhance their understanding of crucial data collection and quality evaluation practices.

Engage with leading experts in the field and broaden your perspective on AI data management.

Syllabus

• Introduction to Data for Research
Importance of quality data in AI
Overview of data requirements for AI applications
Data in the context of security and other fields
• Challenges in Data Acquisition
Identifying data sources
Ethical and legal considerations
Privacy concerns and regulations
• Data Acquisition Methods
Data scraping and web harvesting
API integration for data access
Utilization of public datasets
Crowdsourcing and data collection from users
• Data Quality Assessment
Criteria for high-quality data
Techniques for data validation and cleaning
Handling missing and inconsistent data
• Tools and Technologies for Data Management
Overview of data management systems
Introduction to data warehousing and data lakes
Utilization of open-source tools for managing datasets
• Case Studies
Applications in security
Applications in healthcare
Applications in finance
• Hands-on Projects
Designing a data acquisition strategy for a specific AI application
Performing a data quality assessment and cleaning
Building a small-scale data-centric AI model using acquired data
• Conclusion and Future Directions
Emerging trends in data acquisition
The evolving landscape of data privacy and security
Future technologies and methodologies in data-driven research

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Subjects

Conference Talks